SSSGAN: Satellite Style and Structure Generative Adversarial Networks
نویسندگان
چکیده
This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect segmentation map structure, in addition global descriptor vectors capture semantic information vector Open Street Maps (OSM) classes, this is able produce consistent aerial imagery. By decoupling generation images into structure carefully defined style vector, we were improve realism geodiversity synthesis state-of-the-art baseline. Therefore, proposed allows us control not only desired but also geographic area.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13193984